Calcium imaging experiments that monitor neural activity produce terabytes of raw data. Analysis of these large datasets requires rigorous data management practices for scientific reproducibility. To address this challenge, in this work we present an open-source data pipeline for calcium imaging analysis, as part of a major initiative to assemble and disseminate standardized software for neuroscience experiments. The DataJoint Element for Calcium Imaging supports several popular acquisition systems (ScanImage, Scanbox, and Nikon) and analysis packages (Suite2p, CaImAn, and FISSA). This data pipeline is built atop the DataJoint framework that provides Python and MATLAB interfaces for automating computations. The results are organized in a relational database defining and enforcing data structure, data integrity, and data consistency principles. For collaborative neuroscience projects, we discuss the best practices for operating a calcium imaging pipeline.